@phdthesis{Cabral2009, author = {Cabral, Juliano Sarmento}, title = {Demographic processes determining the range dynamics of plant species, and their consequences for biodiversity maintenance in the face of environmental change}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-41188}, school = {Universit{\"a}t Potsdam}, year = {2009}, abstract = {The present thesis aims to introduce process-based model for species range dynamics that can be fitted to abundance data. For this purpose, the well-studied Proteaceae species of the South African Cape Floristic Region (CFR) offer a great data set to fit process-based models. These species are subject to wildflower harvesting and environmental threats like habitat loss and climate change. The general introduction of this thesis presents shortly the available models for species distribution modelling. Subsequently, it presents the feasibility of process-based modelling. Finally, it introduces the study system as well as the objectives and layout. In Chapter 1, I present the process-based model for range dynamics and a statistical framework to fit it to abundance distribution data. The model has a spatially-explicit demographic submodel (describing dispersal, reproduction, mortality and local extinction) and an observation submodel (describing imperfect detection of individuals). The demographic submodel links species-specific habitat models describing the suitable habitat and process-based demographic models that consider local dynamics and anemochoric seed dispersal between populations. After testing the fitting framework with simulated data, I applied it to eight Proteaceae species with different demographic properties. Moreover, I assess the role of two other demographic mechanisms: positive (Allee effects) and negative density-dependence. Results indicate that Allee effects and overcompensatory local dynamics (including chaotic behaviour) seem to be important for several species. Most parameter estimates quantitatively agreed with independent data. Hence, the presented approach seemed to suit the demand of investigating non-equilibrium scenarios involving wildflower harvesting (Chapter 2) and environmental change (Chapter 3). The Chapter 2 addresses the impacts of wildflower harvesting. The chapter includes a sensitivity analysis over multiple spatial scales and demographic properties (dispersal ability, strength of Allee effects, maximum reproductive rate, adult mortality, local extinction probability and carrying capacity). Subsequently, harvesting effects are investigated on real case study species. Plant response to harvesting showed abrupt threshold behavior. Species with short-distance seed dispersal, strong Allee effects, low maximum reproductive rate, high mortality and high local extinction are most affected by harvesting. Larger spatial scales benefit species response, but the thresholds become sharper. The three case study species supported very low to moderate harvesting rates. Summarizing, demographic knowledge about the study system and careful identification of the spatial scale of interest should guide harvesting assessments and conservation of exploited species. The sensitivity analysis' results can be used to qualitatively assess harvesting impacts for poorly studied species. I investigated in Chapter 3 the consequences of past habitat loss, future climate change and their interaction on plant response. I use the species-specific estimates of the best model describing local dynamics obtained in Chapter 1. Both habitat loss and climate change had strong negative impacts on species dynamics. Climate change affected mainly range size and range filling due to habitat reductions and shifts combined with low colonization. Habitat loss affected mostly local abundances. The scenario with both habitat loss and climate change was the worst for most species. However, this impact was better than expected by simple summing of separate effects of habitat loss and climate change. This is explained by shifting ranges to areas less affected by humans. Range size response was well predicted by the strength of environmental change, whereas range filling and local abundance responses were better explained by demographic properties. Hence, risk assessments under global change should consider demographic properties. Most surviving populations were restricted to refugia, serving as key conservation focus.The findings obtained for the study system as well as the advantages, limitations and potentials of the model presented here are further discussed in the General Discussion. In summary, the results indicate that 1) process-based demographic models for range dynamics can be fitted to data; 2) demographic processes improve species distribution models; 3) different species are subject to different processes and respond differently to environmental change and exploitation; 4) density regulation type and Allee effects should be considered when investigating range dynamics of species; 5) the consequences of wildflower harvesting, habitat loss and climate change could be disastrous for some species, but impacts vary depending on demographic properties; 6) wildflower harvesting impacts varies over spatial scale; 7) The effects of habitat loss and climate change are not always additive.}, language = {en} } @article{MarionMcInernyPageletal.2012, author = {Marion, Glenn and McInerny, Greg J. and Pagel, J{\"o}rn and Catterall, Stephen and Cook, Alex R. and Hartig, Florian and O\&rsquo, and Hara, Robert B.}, title = {Parameter and uncertainty estimation for process-oriented population and distribution models: data, statistics and the niche}, series = {JOURNAL OF BIOGEOGRAPHY}, volume = {39}, journal = {JOURNAL OF BIOGEOGRAPHY}, number = {12}, publisher = {WILEY-BLACKWELL}, address = {HOBOKEN}, issn = {0305-0270}, doi = {10.1111/j.1365-2699.2012.02772.x}, pages = {2225 -- 2239}, year = {2012}, abstract = {The spatial distribution of a species is determined by dynamic processes such as reproduction, mortality and dispersal. Conventional static species distribution models (SDMs) do not incorporate these processes explicitly. This limits their applicability, particularly for non-equilibrium situations such as invasions or climate change. In this paper we show how dynamic SDMs can be formulated and fitted to data within a Bayesian framework. Our focus is on discrete state-space Markov process models which provide a flexible framework to account for stochasticity in key demographic processes, including dispersal, growth and competition. We show how to construct likelihood functions for such models (both discrete and continuous time versions) and how these can be combined with suitable observation models to conduct Bayesian parameter inference using computational techniques such as Markov chain Monte Carlo. We illustrate the current state-of-the-art with three contrasting examples using both simulated and empirical data. The use of simulated data allows the robustness of the methods to be tested with respect to deficiencies in both data and model. These examples show how mechanistic understanding of the processes that determine distribution and abundance can be combined with different sources of information at a range of spatial and temporal scales. Application of such techniques will enable more reliable inference and projections, e.g. under future climate change scenarios than is possible with purely correlative approaches. Conversely, confronting such process-oriented niche models with abundance and distribution data will test current understanding and may ultimately feedback to improve underlying ecological theory.}, language = {en} } @article{SarmentoJeltschThuilleretal.2013, author = {Sarmento, Juliano Sarmento and Jeltsch, Florian and Thuiller, Wilfried and Higgins, Steven and Midgley, Guy F. and Rebelo, Anthony G. and Rouget, Mathieu and Schurr, Frank Martin}, title = {Impacts of past habitat loss and future climate change on the range dynamics of South African Proteaceae}, series = {Diversity \& distributions : a journal of biological invasions and biodiversity}, volume = {19}, journal = {Diversity \& distributions : a journal of biological invasions and biodiversity}, number = {4}, publisher = {Wiley-Blackwell}, address = {Hoboken}, issn = {1366-9516}, doi = {10.1111/ddi.12011}, pages = {363 -- 376}, year = {2013}, abstract = {Aim To assess how habitat loss and climate change interact in affecting the range dynamics of species and to quantify how predicted range dynamics depend on demographic properties of species and the severity of environmental change. Location South African Cape Floristic Region. Methods We use data-driven demographic models to assess the impacts of past habitat loss and future climate change on range size, range filing and abundances of eight species of woody plants (Proteaceae). The species-specific models employ a hybrid approach that simulates population dynamics and long-distance dispersal on top of expected spatio-temporal dynamics of suitable habitat. Results Climate change was mainly predicted to reduce range size and range filling (because of a combination of strong habitat shifts with low migration ability). In contrast, habitat loss mostly decreased mean local abundance. For most species and response measures, the combination of habitat loss and climate change had the most severe effect. Yet, this combined effect was mostly smaller than expected from adding or multiplying effects of the individual environmental drivers. This seems to be because climate change shifts suitable habitats to regions less affected by habitat loss. Interspecific variation in range size responses depended mostly on the severity of environmental change, whereas responses in range filling and local abundance depended mostly on demographic properties of species. While most surviving populations concentrated in areas that remain climatically suitable, refugia for multiple species were overestimated by simply overlying habitat models and ignoring demography. Main conclusions Demographic models of range dynamics can simultaneously predict the response of range size, abundance and range filling to multiple drivers of environmental change. Demographic knowledge is particularly needed to predict abundance responses and to identify areas that can serve as biodiversity refugia under climate change. These findings highlight the need for data-driven, demographic assessments in conservation biogeography.}, language = {en} } @phdthesis{Zurell2011, author = {Zurell, Damaris}, title = {Integrating dynamic and statistical modelling approaches in order to improve predictions for scenarios of environmental change}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-56845}, school = {Universit{\"a}t Potsdam}, year = {2011}, abstract = {Species respond to environmental change by dynamically adjusting their geographical ranges. Robust predictions of these changes are prerequisites to inform dynamic and sustainable conservation strategies. Correlative species distribution models (SDMs) relate species' occurrence records to prevailing environmental factors to describe the environmental niche. They have been widely applied in global change context as they have comparably low data requirements and allow for rapid assessments of potential future species' distributions. However, due to their static nature, transient responses to environmental change are essentially ignored in SDMs. Furthermore, neither dispersal nor demographic processes and biotic interactions are explicitly incorporated. Therefore, it has often been suggested to link statistical and mechanistic modelling approaches in order to make more realistic predictions of species' distributions for scenarios of environmental change. In this thesis, I present two different ways of such linkage. (i) Mechanistic modelling can act as virtual playground for testing statistical models and allows extensive exploration of specific questions. I promote this 'virtual ecologist' approach as a powerful evaluation framework for testing sampling protocols, analyses and modelling tools. Also, I employ such an approach to systematically assess the effects of transient dynamics and ecological properties and processes on the prediction accuracy of SDMs for climate change projections. That way, relevant mechanisms are identified that shape the species' response to altered environmental conditions and which should hence be considered when trying to project species' distribution through time. (ii) I supplement SDM projections of potential future habitat for black grouse in Switzerland with an individual-based population model. By explicitly considering complex interactions between habitat availability and demographic processes, this allows for a more direct assessment of expected population response to environmental change and associated extinction risks. However, predictions were highly variable across simulations emphasising the need for principal evaluation tools like sensitivity analysis to assess uncertainty and robustness in dynamic range predictions. Furthermore, I identify data coverage of the environmental niche as a likely cause for contrasted range predictions between SDM algorithms. SDMs may fail to make reliable predictions for truncated and edge niches, meaning that portions of the niche are not represented in the data or niche edges coincide with data limits. Overall, my thesis contributes to an improved understanding of uncertainty factors in predictions of range dynamics and presents ways how to deal with these. Finally I provide preliminary guidelines for predictive modelling of dynamic species' response to environmental change, identify key challenges for future research and discuss emerging developments.}, language = {en} } @misc{ZurellElithSchroederEsselbach2012, author = {Zurell, Damaris and Elith, Jane and Schr{\"o}der-Esselbach, Boris}, title = {Predicting to new environments tools for visualizing model behaviour and impacts on mapped distributions}, series = {Diversity \& distributions : a journal of biological invasions and biodiversity}, volume = {18}, journal = {Diversity \& distributions : a journal of biological invasions and biodiversity}, number = {6}, publisher = {Wiley-Blackwell}, address = {Hoboken}, issn = {1366-9516}, doi = {10.1111/j.1472-4642.2012.00887.x}, pages = {628 -- 634}, year = {2012}, abstract = {Data limitations can lead to unrealistic fits of predictive species distribution models (SDMs) and spurious extrapolation to novel environments. Here, we want to draw attention to novel combinations of environmental predictors that are within the sampled range of individual predictors but are nevertheless outside the sample space. These tend to be overlooked when visualizing model behaviour. They may be a cause of differing model transferability and environmental change predictions between methods, a problem described in some studies but generally not well understood. We here use a simple simulated data example to illustrate the problem and provide new and complementary visualization techniques to explore model behaviour and predictions to novel environments. We then apply these in a more complex real-world example. Our results underscore the necessity of scrutinizing model fits, ecological theory and environmental novelty.}, language = {en} }